Studierstube Tracker

Availability

This page is an overview of our in-house technology, which is not available to the public. For an overview of available AR frameworks from our group check this page: Framework availability

Overview

Studierstube Tracker is a computer vision library for detection and pose estimation of 2D fiducial markers. It is a successor to the well known ARToolKitPlus library. Its concept is very similar to that of ARToolKit (ARTK), ARToolKitPlus (ARTK+) and ARTag, but its code base is completely different. Studierstube Tracker has been written from scratch with high performance for PCs as well as mobile phones in mind.

How does it compare to ARToolKitPlus?

Studierstube Tracker has been developed from scratch after 4 years of experience with ARToolKitPlus. It was designed from ground up to support mobile phones as well as PCs. Hence, its memory requirements are very low (100KB, 5-10% of ARTK+) and processing is very fast (about twice as fast as ARTK+ on mobile phones, ~1ms per frame on a PC). While ARTK+ follows a monolithic approach, requiring configuration at compile time, Studierstube Tracker is highly modular: Developers can extend Studierstube Tracker in any way, creating new features for it. Other than ARTK+, Studierstube Tracker is not open source though.

Studierstube Tracker does not share any source code with ARToolKitPlus. It is a separate project and has no legal connections to ARToolKit or ARToolKitPlus.

Feature Overview

Studierstube Tracker's main features over other solutions are:

Fully class-based API

Many different marker types

Template markers (ARTK-style)

ID-markers (simple-id & BCH)

DataMatrix markers (see example 2 below)

Frame markers (see examples 3 & 4 below)

Split markers (see example 5)

Grid markers (see example 6)

Highest performance on low-end devices
Studierstube Tracker is the fastest solution for tracking on mobile phones. It is about twice as fast as ARTK+, which used to be the fastest marker tracking library so far. At the same time tracking on phones is much more stable (less jitter) than with ARTK+.
Benchmarks show that up to 185 images per second can be tracked on a 312Mhz smartphone (see below).

Low memory consumption
The memory footprint of Studierstube Tracker mostly depends on the actual camera resolution (1 byte per pixel required). For a typical phone setup with a video stream of 320x240 pixels in YUV12 format the memory usage is below 100KB. This is only 5-10% of the memory usage of ARTK+.

Small Executable
Studierstube Tracker.dll for Windows CE devices is only ~270KB storage.

Up to 4096 id-based markers
The BCH marker system allows up to 4096 markers that are reliably detected at no speed penalty due to large number of markers. No markers need to be trained.

Up to 4 million frame markers
Our new frame markers (FrameMarker2) can encode 22 bits of data and correct up to 3 wrong bits.

Camera pixel-formats (RGB24, RGB32, RGB565, YUV12)
Studierstube Tracker natively supports pixel formats that are common on mobile phones such as RGB565 or YUV12.

Variable marker border width
The marker border with can be modified freely giving more design choices.

High-quality pose estimation gives more stable tracking (less jitter).
Studierstube Tracker includes an implementation of "Robust Planar Pose" by G. Schweighofer and A. Pinz
This mode is only available on the PC version of Studierstube Tracker.

Support for MATLAB camera calibration toolbox
Usage of the MATLAB camera calibration toolbox gives high accuracy camera calibration and works with any device (PC, mobile phone, etc).

Fully configurable at runtime
All aspects of the tracking pipeline can be configured at runtime.

No limits to video resolution
There are no limits to the size of input images (except for memory consumption; 1 byte per pixel). Arbitrarily sized images can be used for tracking. Images with 8 Megapixels and 2000 markers have been successfully tested.

Easy to extend
Studierstube Tracker's was designed from ground up to be easily extensible. Every step of the tracking pipeline is configurable and can be exchanged with custom code - even when using the binary distribution of Studierstube Tracker.

Highly portable
Studierstube Tracker's code base is highly portable since it does not depend on any external libraries except our own Studierstube Core & Math libraries. It further obeys restrictions specific to mobile phones for optimal portability.

Platform Support

Studierstube Tracker currently runs on

Windows XP

Windows CE & Windows Mobile

Symbian, Linux

MacOS

iPhone.

Extendibility

Studierstube Tracker is completely modular. Every part of the pipeline is implemented as a separate class, called a "Feature". The following features make up the complete tracking pipeline:

Thresholding

Fiducial detection

Marker detection

Corner filtering

Pose estimation

Pose filtering

What does Studierstube Tracker not do?

Studierstube Tracker is a marker tracking library. It analyzes camera images and reports the relative pose to markers found in the image.

It does NOT

Read images from a camera

Render anything

Track natural features

Depend on or support any hardware units

Examples

Example 1 : Benchmark image. Tracked at a speed of
185 images/sec on a Motorola Q phone with 312 Mhz.